Predicting regional self-identification from spatial network models

Zack W. Almquist, Carter T. Butts

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Social scientists characterize social life as a hierarchy of environments, from the microlevel of an individual's knowledge and perceptions to the macrolevel of large-scale social networks. In accordance with this typology, individuals are typically thought to reside in micro- and macrolevel structures, composed of multifaceted relations (e.g., acquaintanceship, friendship, and kinship). This article analyzes the effects of social structure on micro outcomes through the case of regional identification. Self-identification occurs in many different domains, one of which is regional; that is, the identification of oneself with a locationally associated group (e.g., a "New Yorker" or "Parisian"). Here, regional self-identification is posited to result from an influence process based on the location of an individual's alters (e.g., friends, kin, or coworkers), such that one tends to identify with regions in which many of his or her alters reside. The structure of this article is laid out as follows: initially, we begin with a discussion of the relevant social science literature for both social networks and identification. This discussion is followed with one about competing mechanisms for regional identification that are motivated first from the social network literature, and second by the social psychological and cognitive literature of decision making and heuristics. Next, the article covers the data and methods employed to test the proposed mechanisms. Finally, the article concludes with a discussion of its findings and further implications for the larger social science literature.

Original languageEnglish (US)
Pages (from-to)50-72
Number of pages23
JournalGeographical Analysis
Volume47
Issue number1
DOIs
StatePublished - Jan 1 2015

Fingerprint Dive into the research topics of 'Predicting regional self-identification from spatial network models'. Together they form a unique fingerprint.

Cite this